Overview

Dataset statistics

Number of variables18
Number of observations3630
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory510.6 KiB
Average record size in memory144.0 B

Variable types

Numeric10
Categorical8

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
Target is highly overall correlated with curricular_units_1st_sem_approved and 3 other fieldsHigh correlation
admission_grade is highly overall correlated with previous_qualification_gradeHigh correlation
curricular_units_1st_sem_approved is highly overall correlated with Target and 3 other fieldsHigh correlation
curricular_units_1st_sem_grade is highly overall correlated with Target and 3 other fieldsHigh correlation
curricular_units_2nd_sem_approved is highly overall correlated with Target and 3 other fieldsHigh correlation
curricular_units_2nd_sem_grade is highly overall correlated with Target and 3 other fieldsHigh correlation
previous_qualification_grade is highly overall correlated with admission_gradeHigh correlation
educational_special_needs is highly imbalanced (91.3%)Imbalance
curricular_units_1st_sem_approved has 647 (17.8%) zerosZeros
curricular_units_1st_sem_grade has 647 (17.8%) zerosZeros
curricular_units_2nd_sem_approved has 802 (22.1%) zerosZeros
curricular_units_2nd_sem_grade has 802 (22.1%) zerosZeros
curricular_units_2nd_sem_without_evaluations has 3416 (94.1%) zerosZeros

Reproduction

Analysis started2026-01-13 13:11:26.614153
Analysis finished2026-01-13 13:12:12.479617
Duration45.87 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

marital_status
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1842975
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-13T18:12:12.634543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61300882
Coefficient of variation (CV)0.51761387
Kurtosis20.916507
Mean1.1842975
Median Absolute Deviation (MAD)0
Skewness4.3378617
Sum4299
Variance0.37577982
MonotonicityNot monotonic
2026-01-13T18:12:12.825392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
13199
88.1%
2327
 
9.0%
475
 
2.1%
522
 
0.6%
65
 
0.1%
32
 
0.1%
ValueCountFrequency (%)
13199
88.1%
2327
 
9.0%
32
 
0.1%
475
 
2.1%
522
 
0.6%
65
 
0.1%
ValueCountFrequency (%)
65
 
0.1%
522
 
0.6%
475
 
2.1%
32
 
0.1%
2327
 
9.0%
13199
88.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size205.7 KiB
1
3222 
0
408 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
13222
88.8%
0408
 
11.2%

Length

2026-01-13T18:12:13.067286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:12:13.242150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
13222
88.8%
0408
 
11.2%

Most occurring characters

ValueCountFrequency (%)
13222
88.8%
0408
 
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
13222
88.8%
0408
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
13222
88.8%
0408
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
13222
88.8%
0408
 
11.2%

previous_qualification
Real number (ℝ)

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5322314
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-13T18:12:13.408021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile39
Maximum43
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.024134
Coefficient of variation (CV)2.2117436
Kurtosis7.0651029
Mean4.5322314
Median Absolute Deviation (MAD)0
Skewness2.9042661
Sum16452
Variance100.48325
MonotonicityNot monotonic
2026-01-13T18:12:13.637870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
13019
83.2%
39164
 
4.5%
19149
 
4.1%
3122
 
3.4%
1239
 
1.1%
4034
 
0.9%
4228
 
0.8%
222
 
0.6%
615
 
0.4%
911
 
0.3%
Other values (7)27
 
0.7%
ValueCountFrequency (%)
13019
83.2%
222
 
0.6%
3122
 
3.4%
47
 
0.2%
51
 
< 0.1%
615
 
0.4%
911
 
0.3%
104
 
0.1%
1239
 
1.1%
141
 
< 0.1%
ValueCountFrequency (%)
436
 
0.2%
4228
 
0.8%
4034
 
0.9%
39164
4.5%
386
 
0.2%
19149
4.1%
152
 
0.1%
141
 
< 0.1%
1239
 
1.1%
104
 
0.1%

previous_qualification_grade
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.92061
Minimum95
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-13T18:12:13.907725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile110
Q1125
median133.1
Q3140
95-th percentile158
Maximum190
Range95
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.238373
Coefficient of variation (CV)0.09959609
Kurtosis0.89457229
Mean132.92061
Median Absolute Deviation (MAD)7.1
Skewness0.28762278
Sum482501.8
Variance175.25451
MonotonicityNot monotonic
2026-01-13T18:12:14.204534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133.1426
 
11.7%
130302
 
8.3%
140271
 
7.5%
120225
 
6.2%
150137
 
3.8%
12592
 
2.5%
11088
 
2.4%
13585
 
2.3%
16079
 
2.2%
13178
 
2.1%
Other values (91)1847
50.9%
ValueCountFrequency (%)
951
 
< 0.1%
961
 
< 0.1%
971
 
< 0.1%
991
 
< 0.1%
10062
1.7%
1013
 
0.1%
1025
 
0.1%
1032
 
0.1%
1053
 
0.1%
1066
 
0.2%
ValueCountFrequency (%)
1901
 
< 0.1%
1881
 
< 0.1%
184.41
 
< 0.1%
1821
 
< 0.1%
1807
0.2%
1782
 
0.1%
1772
 
0.1%
1761
 
< 0.1%
1751
 
< 0.1%
1741
 
< 0.1%

admission_grade
Real number (ℝ)

High correlation 

Distinct602
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.29394
Minimum95
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-13T18:12:14.491320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile103.5
Q1118
median126.5
Q3135.1
95-th percentile154.21
Maximum190
Range95
Interquartile range (IQR)17.1

Descriptive statistics

Standard deviation14.611295
Coefficient of variation (CV)0.1147839
Kurtosis0.56734312
Mean127.29394
Median Absolute Deviation (MAD)8.5
Skewness0.50766763
Sum462077
Variance213.48995
MonotonicityNot monotonic
2026-01-13T18:12:14.985003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130125
 
3.4%
120121
 
3.3%
140117
 
3.2%
10093
 
2.6%
15067
 
1.8%
11065
 
1.8%
16038
 
1.0%
128.235
 
1.0%
12825
 
0.7%
12723
 
0.6%
Other values (592)2921
80.5%
ValueCountFrequency (%)
9510
0.3%
95.11
 
< 0.1%
95.52
 
0.1%
95.81
 
< 0.1%
965
0.1%
96.11
 
< 0.1%
96.71
 
< 0.1%
975
0.1%
97.21
 
< 0.1%
97.41
 
< 0.1%
ValueCountFrequency (%)
1902
0.1%
184.41
< 0.1%
1841
< 0.1%
183.51
< 0.1%
180.41
< 0.1%
1802
0.1%
179.61
< 0.1%
178.31
< 0.1%
1781
< 0.1%
176.71
< 0.1%

is_displaced
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size205.7 KiB
1
1993 
0
1637 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
11993
54.9%
01637
45.1%

Length

2026-01-13T18:12:15.308796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:12:15.465723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11993
54.9%
01637
45.1%

Most occurring characters

ValueCountFrequency (%)
11993
54.9%
01637
45.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11993
54.9%
01637
45.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11993
54.9%
01637
45.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11993
54.9%
01637
45.1%

educational_special_needs
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size205.7 KiB
0
3590 
1
 
40

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03590
98.9%
140
 
1.1%

Length

2026-01-13T18:12:15.662566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:12:15.811473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03590
98.9%
140
 
1.1%

Most occurring characters

ValueCountFrequency (%)
03590
98.9%
140
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03590
98.9%
140
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03590
98.9%
140
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03590
98.9%
140
 
1.1%

is_debtor
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size205.7 KiB
0
3217 
1
413 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03217
88.6%
1413
 
11.4%

Length

2026-01-13T18:12:15.998380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:12:16.151278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03217
88.6%
1413
 
11.4%

Most occurring characters

ValueCountFrequency (%)
03217
88.6%
1413
 
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03217
88.6%
1413
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03217
88.6%
1413
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03217
88.6%
1413
 
11.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size205.7 KiB
1
3144 
0
486 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
13144
86.6%
0486
 
13.4%

Length

2026-01-13T18:12:16.349124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:12:16.499029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
13144
86.6%
0486
 
13.4%

Most occurring characters

ValueCountFrequency (%)
13144
86.6%
0486
 
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
13144
86.6%
0486
 
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
13144
86.6%
0486
 
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
13144
86.6%
0486
 
13.4%

gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size205.7 KiB
0
2381 
1
1249 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02381
65.6%
11249
34.4%

Length

2026-01-13T18:12:16.699902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:12:16.853802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02381
65.6%
11249
34.4%

Most occurring characters

ValueCountFrequency (%)
02381
65.6%
11249
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02381
65.6%
11249
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02381
65.6%
11249
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02381
65.6%
11249
34.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size205.7 KiB
0
2661 
1
969 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02661
73.3%
1969
 
26.7%

Length

2026-01-13T18:12:17.057694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:12:17.207574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02661
73.3%
1969
 
26.7%

Most occurring characters

ValueCountFrequency (%)
02661
73.3%
1969
 
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02661
73.3%
1969
 
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02661
73.3%
1969
 
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02661
73.3%
1969
 
26.7%

age_at_enrollment
Real number (ℝ)

Distinct46
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.461157
Minimum17
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-13T18:12:17.714245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile18
Q119
median20
Q325
95-th percentile41
Maximum70
Range53
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.8279942
Coefficient of variation (CV)0.33365764
Kurtosis3.8030507
Mean23.461157
Median Absolute Deviation (MAD)2
Skewness1.9907248
Sum85164
Variance61.277493
MonotonicityNot monotonic
2026-01-13T18:12:18.000064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
18864
23.8%
19754
20.8%
20459
12.6%
21252
 
6.9%
22137
 
3.8%
24101
 
2.8%
2384
 
2.3%
2779
 
2.2%
2675
 
2.1%
2572
 
2.0%
Other values (36)753
20.7%
ValueCountFrequency (%)
173
 
0.1%
18864
23.8%
19754
20.8%
20459
12.6%
21252
 
6.9%
22137
 
3.8%
2384
 
2.3%
24101
 
2.8%
2572
 
2.0%
2675
 
2.1%
ValueCountFrequency (%)
701
 
< 0.1%
621
 
< 0.1%
611
 
< 0.1%
602
 
0.1%
593
0.1%
583
0.1%
572
 
0.1%
555
0.1%
546
0.2%
536
0.2%

curricular_units_1st_sem_approved
Real number (ℝ)

High correlation  Zeros 

Distinct23
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7914601
Minimum0
Maximum26
Zeros647
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-13T18:12:18.241932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q36
95-th percentile10
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2378452
Coefficient of variation (CV)0.67575335
Kurtosis2.8666924
Mean4.7914601
Median Absolute Deviation (MAD)1
Skewness0.75417833
Sum17393
Variance10.483642
MonotonicityNot monotonic
2026-01-13T18:12:18.466761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
61033
28.5%
0647
17.8%
5530
14.6%
7429
11.8%
4288
 
7.9%
3176
 
4.8%
2118
 
3.3%
194
 
2.6%
894
 
2.6%
1142
 
1.2%
Other values (13)179
 
4.9%
ValueCountFrequency (%)
0647
17.8%
194
 
2.6%
2118
 
3.3%
3176
 
4.8%
4288
 
7.9%
5530
14.6%
61033
28.5%
7429
11.8%
894
 
2.6%
935
 
1.0%
ValueCountFrequency (%)
261
 
< 0.1%
214
 
0.1%
203
 
0.1%
192
 
0.1%
1815
0.4%
1710
0.3%
165
 
0.1%
156
 
0.2%
1414
0.4%
1323
0.6%

curricular_units_1st_sem_grade
Real number (ℝ)

High correlation  Zeros 

Distinct752
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.53486
Minimum0
Maximum18.875
Zeros647
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-13T18:12:18.729591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median12.341429
Q313.5
95-th percentile15
Maximum18.875
Range18.875
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation5.057694
Coefficient of variation (CV)0.48009125
Kurtosis0.48687085
Mean10.53486
Median Absolute Deviation (MAD)1.1985714
Skewness-1.4518527
Sum38241.54
Variance25.580268
MonotonicityNot monotonic
2026-01-13T18:12:19.035399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0647
 
17.8%
12153
 
4.2%
13123
 
3.4%
1195
 
2.6%
1470
 
1.9%
12.3333333368
 
1.9%
12.6666666767
 
1.8%
12.566
 
1.8%
11.564
 
1.8%
1060
 
1.7%
Other values (742)2217
61.1%
ValueCountFrequency (%)
0647
17.8%
9.81
 
< 0.1%
1060
 
1.7%
10.166666671
 
< 0.1%
10.23
 
0.1%
10.214285711
 
< 0.1%
10.257
 
0.2%
10.285714291
 
< 0.1%
10.333333337
 
0.2%
10.368421051
 
< 0.1%
ValueCountFrequency (%)
18.8751
 
< 0.1%
182
0.1%
17.333333332
0.1%
17.1251
 
< 0.1%
17.111111111
 
< 0.1%
17.005555561
 
< 0.1%
173
0.1%
16.91
 
< 0.1%
16.885714291
 
< 0.1%
16.857142861
 
< 0.1%

curricular_units_2nd_sem_approved
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5184573
Minimum0
Maximum20
Zeros802
Zeros (%)22.1%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-13T18:12:19.292229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q36
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1623763
Coefficient of variation (CV)0.69987964
Kurtosis0.66566854
Mean4.5184573
Median Absolute Deviation (MAD)2
Skewness0.26819875
Sum16402
Variance10.000624
MonotonicityNot monotonic
2026-01-13T18:12:19.515085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6867
23.9%
0802
22.1%
5569
15.7%
8306
 
8.4%
7287
 
7.9%
4254
 
7.0%
3161
 
4.4%
2121
 
3.3%
186
 
2.4%
1144
 
1.2%
Other values (10)133
 
3.7%
ValueCountFrequency (%)
0802
22.1%
186
 
2.4%
2121
 
3.3%
3161
 
4.4%
4254
 
7.0%
5569
15.7%
6867
23.9%
7287
 
7.9%
8306
 
8.4%
925
 
0.7%
ValueCountFrequency (%)
202
 
0.1%
193
 
0.1%
182
 
0.1%
178
 
0.2%
162
 
0.1%
146
 
0.2%
1321
0.6%
1232
0.9%
1144
1.2%
1032
0.9%

curricular_units_2nd_sem_grade
Real number (ℝ)

High correlation  Zeros 

Distinct724
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.036155
Minimum0
Maximum18.571429
Zeros802
Zeros (%)22.1%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-13T18:12:19.785915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.517857
median12.333333
Q313.5
95-th percentile15.006111
Maximum18.571429
Range18.571429
Interquartile range (IQR)2.9821429

Descriptive statistics

Standard deviation5.4817421
Coefficient of variation (CV)0.54619942
Kurtosis-0.36292579
Mean10.036155
Median Absolute Deviation (MAD)1.3333333
Skewness-1.167812
Sum36431.243
Variance30.049496
MonotonicityNot monotonic
2026-01-13T18:12:20.082720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0802
 
22.1%
12122
 
3.4%
13117
 
3.2%
11111
 
3.1%
1463
 
1.7%
11.562
 
1.7%
12.555
 
1.5%
1051
 
1.4%
13.550
 
1.4%
12.6666666749
 
1.3%
Other values (714)2148
59.2%
ValueCountFrequency (%)
0802
22.1%
1051
 
1.4%
10.166666673
 
0.1%
10.22
 
0.1%
10.255
 
0.1%
10.3333333310
 
0.3%
10.44
 
0.1%
10.428571431
 
< 0.1%
10.444444441
 
< 0.1%
10.529
 
0.8%
ValueCountFrequency (%)
18.571428571
< 0.1%
17.714285711
< 0.1%
17.692307691
< 0.1%
17.61
< 0.1%
17.58751
< 0.1%
17.428571431
< 0.1%
17.166666671
< 0.1%
171
< 0.1%
16.909090911
< 0.1%
16.82
0.1%
Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14214876
Minimum0
Maximum12
Zeros3416
Zeros (%)94.1%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-13T18:12:20.314574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.74767044
Coefficient of variation (CV)5.2597746
Kurtosis73.264171
Mean0.14214876
Median Absolute Deviation (MAD)0
Skewness7.6154483
Sum516
Variance0.55901109
MonotonicityNot monotonic
2026-01-13T18:12:20.521465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
03416
94.1%
1107
 
2.9%
235
 
1.0%
323
 
0.6%
516
 
0.4%
416
 
0.4%
67
 
0.2%
74
 
0.1%
84
 
0.1%
122
 
0.1%
ValueCountFrequency (%)
03416
94.1%
1107
 
2.9%
235
 
1.0%
323
 
0.6%
416
 
0.4%
516
 
0.4%
67
 
0.2%
74
 
0.1%
84
 
0.1%
122
 
0.1%
ValueCountFrequency (%)
122
 
0.1%
84
 
0.1%
74
 
0.1%
67
 
0.2%
516
 
0.4%
416
 
0.4%
323
 
0.6%
235
 
1.0%
1107
 
2.9%
03416
94.1%

Target
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size205.7 KiB
1
2209 
0
1421 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
12209
60.9%
01421
39.1%

Length

2026-01-13T18:12:20.778271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:12:20.929174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
12209
60.9%
01421
39.1%

Most occurring characters

ValueCountFrequency (%)
12209
60.9%
01421
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12209
60.9%
01421
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12209
60.9%
01421
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12209
60.9%
01421
39.1%

Interactions

2026-01-13T18:12:07.573779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:29.594234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:33.384799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:37.020453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:41.249728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:45.394061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:49.521403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:54.135454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:58.430666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:03.120644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:07.821616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:30.082922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:33.850492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:37.465164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:41.635482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:45.806793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:49.830203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:54.618120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:58.989304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:03.426447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:08.069458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:30.401716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:34.154301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:37.831930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:42.001243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:46.196542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:50.296904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:55.228727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:59.336081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:04.309879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:08.688060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:30.737496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:34.485084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:38.214683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:42.405985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:46.670239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:51.408186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:55.496554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:59.636885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:05.030415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:09.063816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:31.055293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:34.802879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:38.594437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:42.795733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:47.046994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:51.938845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:55.785366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:59.919712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:05.330220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:09.328673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:31.382081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:35.122672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:39.013169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:43.226458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:47.404765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:52.413539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:56.197102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:00.252492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:05.620035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:09.598471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:31.720864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:35.454461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:39.431899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:43.714141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:47.814502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:52.910218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:56.762739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:01.065968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:06.182674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:10.316011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:32.082630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:35.810231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:39.895600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:44.124879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:48.225239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:53.216020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:57.259420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:01.799495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:06.709332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:10.700762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:32.685242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:36.189985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:40.389282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:44.518624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:48.692936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:53.525822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:57.607198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:02.267194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:07.004189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:11.199442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:33.041015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:36.569742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:40.803017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:44.964336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:49.150643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:53.860607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:11:57.977957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:02.656942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:12:07.273972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-13T18:12:21.114055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Targetadmission_gradeage_at_enrollmentcurricular_units_1st_sem_approvedcurricular_units_1st_sem_gradecurricular_units_2nd_sem_approvedcurricular_units_2nd_sem_gradecurricular_units_2nd_sem_without_evaluationsdaytime_evening_attendanceeducational_special_needsgenderis_debtoris_displacedis_scholarship_holdermarital_statusprevious_qualificationprevious_qualification_gradetuition_fees_up_to_date
Target1.0000.1570.3330.6490.5420.7360.6400.1010.0820.0000.2510.2660.1240.3120.1150.1520.1340.441
admission_grade0.1571.000-0.1120.1120.2290.1110.209-0.0260.1120.0000.0430.0680.1390.095-0.0020.1160.5820.088
age_at_enrollment0.333-0.1121.000-0.204-0.243-0.222-0.2470.1160.4810.0000.1950.1400.4020.2400.4880.408-0.1700.232
curricular_units_1st_sem_approved0.6490.112-0.2041.0000.6510.9090.682-0.0590.1850.0000.2640.1670.1530.290-0.074-0.0590.0990.327
curricular_units_1st_sem_grade0.5420.229-0.2430.6511.0000.6360.790-0.0450.1390.0000.2100.1200.0910.209-0.099-0.0620.2020.277
curricular_units_2nd_sem_approved0.7360.111-0.2220.9090.6361.0000.711-0.0580.1080.0000.2750.2020.1290.289-0.081-0.0670.0890.364
curricular_units_2nd_sem_grade0.6400.209-0.2470.6820.7900.7111.000-0.0590.0940.0000.2220.1600.0830.227-0.097-0.0630.1720.327
curricular_units_2nd_sem_without_evaluations0.101-0.0260.116-0.059-0.045-0.058-0.0591.0000.0000.0000.0430.0870.0280.0320.0590.061-0.0240.090
daytime_evening_attendance0.0820.1120.4810.1850.1390.1080.0940.0001.0000.0190.0240.0000.2420.1080.3560.1600.1220.048
educational_special_needs0.0000.0000.0000.0000.0000.0000.0000.0000.0191.0000.0000.0000.0000.0230.0000.0000.0000.000
gender0.2510.0430.1950.2640.2100.2750.2220.0430.0240.0001.0000.0490.1260.1870.0560.1270.0420.120
is_debtor0.2660.0680.1400.1670.1200.2020.1600.0870.0000.0000.0491.0000.0910.0620.0320.1490.0720.433
is_displaced0.1240.1390.4020.1530.0910.1290.0830.0280.2420.0000.1260.0911.0000.0840.2790.1840.1040.103
is_scholarship_holder0.3120.0950.2400.2900.2090.2890.2270.0320.1080.0230.1870.0620.0841.0000.1170.0850.0660.168
marital_status0.115-0.0020.488-0.074-0.099-0.081-0.0970.0590.3560.0000.0560.0320.2790.1171.0000.198-0.0400.104
previous_qualification0.1520.1160.408-0.059-0.062-0.067-0.0630.0610.1600.0000.1270.1490.1840.0850.1981.0000.0440.124
previous_qualification_grade0.1340.582-0.1700.0990.2020.0890.172-0.0240.1220.0000.0420.0720.1040.066-0.0400.0441.0000.105
tuition_fees_up_to_date0.4410.0880.2320.3270.2770.3640.3270.0900.0480.0000.1200.4330.1030.1680.1040.1240.1051.000

Missing values

2026-01-13T18:12:11.661144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-13T18:12:12.136837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

marital_statusdaytime_evening_attendanceprevious_qualificationprevious_qualification_gradeadmission_gradeis_displacededucational_special_needsis_debtortuition_fees_up_to_dategenderis_scholarship_holderage_at_enrollmentcurricular_units_1st_sem_approvedcurricular_units_1st_sem_gradecurricular_units_2nd_sem_approvedcurricular_units_2nd_sem_gradecurricular_units_2nd_sem_without_evaluationsTarget
0111122.0127.31001102000.00000000.00000000
1111160.0142.510001019614.000000613.66666701
2111122.0124.81000101900.00000000.00000000
3111122.0119.610010020613.428571512.40000001
4201100.0141.500010045512.333333613.00000001
52019133.1114.800111050511.857143511.50000051
6111142.0128.410010118713.300000814.34500001
7111119.0113.11000102200.00000000.00000000
8111137.0129.300010121613.875000614.14285701
9111138.0123.010100018511.400000213.50000000
marital_statusdaytime_evening_attendanceprevious_qualificationprevious_qualification_gradeadmission_gradeis_displacededucational_special_needsis_debtortuition_fees_up_to_dategenderis_scholarship_holderage_at_enrollmentcurricular_units_1st_sem_approvedcurricular_units_1st_sem_gradecurricular_units_2nd_sem_approvedcurricular_units_2nd_sem_gradecurricular_units_2nd_sem_without_evaluationsTarget
3620111137.0129.310010018511.800000511.60000001
36214119133.1117.800100046312.333333311.08333300
3622111136.0131.3000100231212.6250001212.62500011
3623111132.0133.810010120613.833333613.50000001
36241139120.0120.000011020612.500000713.14285711
3625111125.0122.200011019513.600000512.66666701
3626111120.0119.010100018612.000000211.00000000
3627111154.0149.510010130714.912500113.50000000
3628111180.0153.810010120513.800000512.00000001
3629111152.0152.010010022611.666667613.00000001

Duplicate rows

Most frequently occurring

marital_statusdaytime_evening_attendanceprevious_qualificationprevious_qualification_gradeadmission_gradeis_displacededucational_special_needsis_debtortuition_fees_up_to_dategenderis_scholarship_holderage_at_enrollmentcurricular_units_1st_sem_approvedcurricular_units_1st_sem_gradecurricular_units_2nd_sem_approvedcurricular_units_2nd_sem_gradecurricular_units_2nd_sem_without_evaluationsTarget# duplicates
0111133.195.00001102000.000.0002